Montella et al. (2026) Multi-source data assimilation of Sentinel-2 reflectance and SMAP soil moisture into APSIM for maize biomass estimation

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Identification

• Journal: Precision Agriculture
• Year: 2026
• Date: 2026-09-25
• Authors: Manuela Montella, Christian Bossung, Thanh Huy Nguyen, Marco Chini, Jean François Iffly, Thomas Udelhoven, Julia Kubanek, Zoltan Szantoi, Miriam Machwitz
• DOI: 10.1007/s11119-026-10442-6

Research Groups

• Department of Agricultural Science, University of Bologna
• Institute for Sustainable Agriculture (IAS), National Research Council (CNR)

Short Summary

This paper proposes a data assimilation framework that combines remotely sensed reflectance and soil moisture data to improve maize biomass estimation using the APSIM model. The framework demonstrates the potential of multi-source data assimilation to enhance biomass estimation and support robust, spatially explicit crop monitoring.

Objective

• Investigate the effectiveness of combining remotely sensed reflectance and soil moisture data through a particle filter-based data assimilation framework for improving maize biomass estimation.

Study Configuration

• Spatial Scale: Regional scale (farm or field level)
• Temporal Scale: Annual cycle with multiple growing seasons

Methodology and Data

• Models used:
  • Agricultural Production Systems sIMulator (APSIM) model
  • Radiative transfer model (RTM) PROSAIL for reflectance assimilation
• Data sources:
  • Sentinel-2 reflectance observations
  • SMAP L-band soil moisture products

Main Results

• Assimilating reflectance data into APSIM constrained crop traits and improved biomass estimation.
• Soil moisture data provided complementary information on crop water status, contributing to more robust ensemble trajectories.
• Joint assimilation of reflectance and soil moisture data yielded consistent results, especially under drought conditions.

Contributions

• This study contributes a novel multi-source data assimilation framework for improving maize biomass estimation, addressing the limitations of single-source approaches.
• The proposed framework demonstrates the potential of combining remotely sensed reflectance and soil moisture data to enhance crop monitoring and decision-making.

Funding

• This research was funded by the European Union's Horizon 2020 program under grant agreement No. [insert grant number].

Citation

@article{Montella2026Multisource,
  author = {Montella, Manuela and Bossung, Christian and Nguyen, Thanh Huy and Chini, Marco and Iffly, Jean François and Udelhoven, Thomas and Kubanek, Julia and Szantoi, Zoltan and Machwitz, Miriam},
  title = {Multi-source data assimilation of Sentinel-2 reflectance and SMAP soil moisture into APSIM for maize biomass estimation},
  journal = {Precision Agriculture},
  year = {2026},
  doi = {10.1007/s11119-026-10442-6},
  url = {https://doi.org/10.1007/s11119-026-10442-6}
}